use burn::{
    tensor::{backend::Backend, Tensor},
};
use std::{
    fs,
    path::{Path, PathBuf},
};

use super::synthetic_dataset::Target;

/// YOLO v1 data loader for training
pub struct YoloDataLoader {
    data_dir: PathBuf,
    split: String,
    batch_size: usize,
    image_size: (u32, u32),
    grid_size: usize,
    num_boxes: usize,
    num_classes: usize,
}

impl YoloDataLoader {
    pub fn new(
        data_dir: &Path,
        split: &str,
        batch_size: usize,
        image_size: (u32, u32),
        grid_size: usize,
        num_boxes: usize,
        num_classes: usize,
    ) -> Self {
        Self {
            data_dir: data_dir.to_path_buf(),
            split: split.to_string(),
            batch_size,
            image_size,
            grid_size,
            num_boxes,
            num_classes,
        }
    }

    /// Load all data items
    pub fn load_data(&self) -> Result<Vec<YoloDataItem>, Box<dyn std::error::Error>> {
        let images_dir = self.data_dir.join("images").join(&self.split);
        let labels_dir = self.data_dir.join("labels").join(&self.split);
        
        let mut items = Vec::new();
        
        // Read all image files
        for entry in fs::read_dir(&images_dir)? {
            let entry = entry?;
            let image_path = entry.path();
            
            if image_path.extension().map_or(false, |ext| ext == "png") {
                // Corresponding label file
                let label_filename = image_path.file_stem()
                    .and_then(|s| s.to_str())
                    .map(|s| format!("{}.txt", s))
                    .ok_or("Invalid filename")?;
                
                let label_path = labels_dir.join(&label_filename);
                
                if label_path.exists() {
                    let targets = self.load_yolo_annotations(&label_path)?;
                    items.push(YoloDataItem {
                        image_path,
                        targets,
                    });
                }
            }
        }
        
        Ok(items)
    }

    /// Load YOLO format annotations
    fn load_yolo_annotations(&self, label_path: &Path) -> Result<Vec<Target>, Box<dyn std::error::Error>> {
        let content = fs::read_to_string(label_path)?;
        let mut targets = Vec::new();
        
        for line in content.lines() {
            let parts: Vec<&str> = line.split_whitespace().collect();
            if parts.len() == 5 {
                let class_id = parts[0].parse::<usize>()?;
                let x = parts[1].parse::<f32>()?;
                let y = parts[2].parse::<f32>()?;
                let width = parts[3].parse::<f32>()?;
                let height = parts[4].parse::<f32>()?;
                
                targets.push(Target {
                    class_id,
                    x,
                    y,
                    width,
                    height,
                });
            }
        }
        
        Ok(targets)
    }

    /// Create batches from data items
    pub fn create_batches(&self, data: &[YoloDataItem]) -> Vec<Vec<YoloDataItem>> {
        data.chunks(self.batch_size)
            .map(|chunk| chunk.to_vec())
            .collect()
    }

    /// Get dataset size
    pub fn len(&self) -> usize {
        match self.load_data() {
            Ok(data) => data.len(),
            Err(_) => 0,
        }
    }

    /// Check if dataset is empty
    pub fn is_empty(&self) -> bool {
        self.len() == 0
    }
}

/// YOLO data item containing image path and targets
#[derive(Debug, Clone)]
pub struct YoloDataItem {
    pub image_path: PathBuf,
    pub targets: Vec<Target>,
}

/// Batcher for converting YOLO data items to tensors
pub struct YoloBatcher<B: Backend> {
    device: B::Device,
    image_size: (u32, u32),
    grid_size: usize,
    num_boxes: usize,
    num_classes: usize,
}

impl<B: Backend> YoloBatcher<B> {
    pub fn new(
        device: B::Device,
        image_size: (u32, u32),
        grid_size: usize,
        num_boxes: usize,
        num_classes: usize,
    ) -> Self {
        Self {
            device,
            image_size,
            grid_size,
            num_boxes,
            num_classes,
        }
    }

    /// Batch multiple items
    pub fn batch(&self, items: &[YoloDataItem]) -> Result<(Tensor<B, 4>, Tensor<B, 4>), Box<dyn std::error::Error>> {
        let batch_size = items.len();
        
        // Create placeholder tensors for demonstration
        // In a real implementation, you would load and preprocess images here
        
        // Image tensor: [batch_size, 3, height, width]
        let images = Tensor::zeros(
            [batch_size, 3, self.image_size.1 as usize, self.image_size.0 as usize],
            &self.device,
        );
        
        // Target tensor: [batch_size, grid_size, grid_size, num_boxes * 5 + num_classes]
        let targets = Tensor::zeros(
            [batch_size, self.grid_size, self.grid_size, self.num_boxes * 5 + self.num_classes],
            &self.device,
        );
        
        Ok((images, targets))
    }

    /// Load and preprocess image (simplified placeholder)
    fn load_and_preprocess_image(&self, _image_path: &Path) -> Result<Tensor<B, 3>, Box<dyn std::error::Error>> {
        // Placeholder implementation
        // In a real implementation, you would:
        // 1. Load image from disk
        // 2. Resize to target size
        // 3. Convert to tensor
        // 4. Normalize pixel values
        
        let image = Tensor::zeros(
            [3, self.image_size.1 as usize, self.image_size.0 as usize],
            &self.device,
        );
        
        Ok(image)
    }

    /// Convert targets to YOLO format tensor (simplified placeholder)
    fn targets_to_tensor(&self, _targets: &[Target]) -> Tensor<B, 3> {
        // Placeholder implementation
        // In a real implementation, you would convert targets to YOLO format
        
        Tensor::zeros(
            [self.grid_size, self.grid_size, self.num_boxes * 5 + self.num_classes],
            &self.device,
        )
    }
}

/// Configuration for YOLO data loader
#[derive(Debug, Clone)]
pub struct YoloDataLoaderConfig {
    pub batch_size: usize,
    pub image_size: (u32, u32),
    pub grid_size: usize,
    pub num_boxes: usize,
    pub num_classes: usize,
}

impl Default for YoloDataLoaderConfig {
    fn default() -> Self {
        Self {
            batch_size: 8,
            image_size: (448, 448),
            grid_size: 7,
            num_boxes: 2,
            num_classes: 3,
        }
    }
}

/// Create training data loader
pub fn create_train_dataloader(
    data_dir: &Path,
    config: YoloDataLoaderConfig,
) -> YoloDataLoader {
    YoloDataLoader::new(
        data_dir,
        "train",
        config.batch_size,
        config.image_size,
        config.grid_size,
        config.num_boxes,
        config.num_classes,
    )
}

/// Create validation data loader
pub fn create_val_dataloader(
    data_dir: &Path,
    config: YoloDataLoaderConfig,
) -> YoloDataLoader {
    YoloDataLoader::new(
        data_dir,
        "val",
        config.batch_size,
        config.image_size,
        config.grid_size,
        config.num_boxes,
        config.num_classes,
    )
}
